
"""simple docstring"""

import os
import tempfile
import unittest
from pathlib import Path

from transformers import AutoConfig, is_torch_available
from transformers.testing_utils import require_torch, torch_device


if is_torch_available():
   from transformers import PyTorchBenchmark, PyTorchBenchmarkArguments
@require_torch
class   _UpperCAmelCase				(		unittest.TestCase				):




      def      a_   (				self ,							lowercase_				)    ->       List[str]:
          for model_result in results.values():
              for batch_size, sequence_length in zip(model_result['bs'] ,							model_result['ss']				):
                  UpperCAmelCase          =       model_result["""result"""][batch_size][sequence_length]
                  self.assertIsNotNone(SCREAMING_SNAKE_CASE_				)




      def      a_   (				self				)    ->       Optional[int]:
          UpperCAmelCase          =       """sshleifer/tiny-gpt2"""
          UpperCAmelCase          =       PyTorchBenchmarkArguments(
              models=[MODEL_ID] ,							training=SCREAMING_SNAKE_CASE_ ,							inference=SCREAMING_SNAKE_CASE_ ,							sequence_lengths=[8] ,							batch_sizes=[1] ,							multi_process=SCREAMING_SNAKE_CASE_ ,							)
          UpperCAmelCase          =       PyTorchBenchmark(SCREAMING_SNAKE_CASE_				)
          UpperCAmelCase          =       benchmark.run()
          self.check_results_dict_not_empty(results.time_inference_result				)
          self.check_results_dict_not_empty(results.memory_inference_result				)




      def      a_   (				self				)    ->       Optional[int]:
          UpperCAmelCase          =       """sgugger/tiny-distilbert-classification"""
          UpperCAmelCase          =       PyTorchBenchmarkArguments(
              models=[MODEL_ID] ,							training=SCREAMING_SNAKE_CASE_ ,							inference=SCREAMING_SNAKE_CASE_ ,							sequence_lengths=[8] ,							batch_sizes=[1] ,							multi_process=SCREAMING_SNAKE_CASE_ ,							only_pretrain_model=SCREAMING_SNAKE_CASE_ ,							)
          UpperCAmelCase          =       PyTorchBenchmark(SCREAMING_SNAKE_CASE_				)
          UpperCAmelCase          =       benchmark.run()
          self.check_results_dict_not_empty(results.time_inference_result				)
          self.check_results_dict_not_empty(results.memory_inference_result				)




      def      a_   (				self				)    ->       Dict:
          UpperCAmelCase          =       """sshleifer/tiny-gpt2"""
          UpperCAmelCase          =       PyTorchBenchmarkArguments(
              models=[MODEL_ID] ,							training=SCREAMING_SNAKE_CASE_ ,							inference=SCREAMING_SNAKE_CASE_ ,							torchscript=SCREAMING_SNAKE_CASE_ ,							sequence_lengths=[8] ,							batch_sizes=[1] ,							multi_process=SCREAMING_SNAKE_CASE_ ,							)
          UpperCAmelCase          =       PyTorchBenchmark(SCREAMING_SNAKE_CASE_				)
          UpperCAmelCase          =       benchmark.run()
          self.check_results_dict_not_empty(results.time_inference_result				)
          self.check_results_dict_not_empty(results.memory_inference_result				)




      @unittest.skipIf(torch_device == 'cpu' ,							'Cant do half precision'				)
      def      a_   (				self				)    ->       int:
          UpperCAmelCase          =       """sshleifer/tiny-gpt2"""
          UpperCAmelCase          =       PyTorchBenchmarkArguments(
              models=[MODEL_ID] ,							training=SCREAMING_SNAKE_CASE_ ,							inference=SCREAMING_SNAKE_CASE_ ,							fpaa=SCREAMING_SNAKE_CASE_ ,							sequence_lengths=[8] ,							batch_sizes=[1] ,							multi_process=SCREAMING_SNAKE_CASE_ ,							)
          UpperCAmelCase          =       PyTorchBenchmark(SCREAMING_SNAKE_CASE_				)
          UpperCAmelCase          =       benchmark.run()
          self.check_results_dict_not_empty(results.time_inference_result				)
          self.check_results_dict_not_empty(results.memory_inference_result				)




      def      a_   (				self				)    ->       str:
          UpperCAmelCase          =       """sshleifer/tiny-gpt2"""
          UpperCAmelCase          =       AutoConfig.from_pretrained(SCREAMING_SNAKE_CASE_				)
          # set architectures equal to `None`
          UpperCAmelCase          =       None
          UpperCAmelCase          =       PyTorchBenchmarkArguments(
              models=[MODEL_ID] ,							training=SCREAMING_SNAKE_CASE_ ,							inference=SCREAMING_SNAKE_CASE_ ,							sequence_lengths=[8] ,							batch_sizes=[1] ,							multi_process=SCREAMING_SNAKE_CASE_ ,							)
          UpperCAmelCase          =       PyTorchBenchmark(SCREAMING_SNAKE_CASE_ ,							configs=[config]				)
          UpperCAmelCase          =       benchmark.run()
          self.check_results_dict_not_empty(results.time_inference_result				)
          self.check_results_dict_not_empty(results.memory_inference_result				)




      def      a_   (				self				)    ->       Any:
          UpperCAmelCase          =       """sshleifer/tiny-gpt2"""
          UpperCAmelCase          =       PyTorchBenchmarkArguments(
              models=[MODEL_ID] ,							training=SCREAMING_SNAKE_CASE_ ,							inference=SCREAMING_SNAKE_CASE_ ,							sequence_lengths=[8] ,							batch_sizes=[1] ,							multi_process=SCREAMING_SNAKE_CASE_ ,							)
          UpperCAmelCase          =       PyTorchBenchmark(SCREAMING_SNAKE_CASE_				)
          UpperCAmelCase          =       benchmark.run()
          self.check_results_dict_not_empty(results.time_train_result				)
          self.check_results_dict_not_empty(results.memory_train_result				)




      @unittest.skipIf(torch_device == 'cpu' ,							'Can\'t do half precision'				)
      def      a_   (				self				)    ->       List[str]:
          UpperCAmelCase          =       """sshleifer/tiny-gpt2"""
          UpperCAmelCase          =       PyTorchBenchmarkArguments(
              models=[MODEL_ID] ,							training=SCREAMING_SNAKE_CASE_ ,							inference=SCREAMING_SNAKE_CASE_ ,							sequence_lengths=[8] ,							batch_sizes=[1] ,							fpaa=SCREAMING_SNAKE_CASE_ ,							multi_process=SCREAMING_SNAKE_CASE_ ,							)
          UpperCAmelCase          =       PyTorchBenchmark(SCREAMING_SNAKE_CASE_				)
          UpperCAmelCase          =       benchmark.run()
          self.check_results_dict_not_empty(results.time_train_result				)
          self.check_results_dict_not_empty(results.memory_train_result				)




      def      a_   (				self				)    ->       List[str]:
          UpperCAmelCase          =       """sshleifer/tiny-gpt2"""
          UpperCAmelCase          =       AutoConfig.from_pretrained(SCREAMING_SNAKE_CASE_				)
          UpperCAmelCase          =       PyTorchBenchmarkArguments(
              models=[MODEL_ID] ,							training=SCREAMING_SNAKE_CASE_ ,							inference=SCREAMING_SNAKE_CASE_ ,							sequence_lengths=[8] ,							batch_sizes=[1] ,							multi_process=SCREAMING_SNAKE_CASE_ ,							)
          UpperCAmelCase          =       PyTorchBenchmark(SCREAMING_SNAKE_CASE_ ,							configs=[config]				)
          UpperCAmelCase          =       benchmark.run()
          self.check_results_dict_not_empty(results.time_inference_result				)
          self.check_results_dict_not_empty(results.memory_inference_result				)




      def      a_   (				self				)    ->       List[Any]:
          UpperCAmelCase          =       """sshleifer/tinier_bart"""
          UpperCAmelCase          =       AutoConfig.from_pretrained(SCREAMING_SNAKE_CASE_				)
          UpperCAmelCase          =       PyTorchBenchmarkArguments(
              models=[MODEL_ID] ,							training=SCREAMING_SNAKE_CASE_ ,							inference=SCREAMING_SNAKE_CASE_ ,							sequence_lengths=[8] ,							batch_sizes=[1] ,							multi_process=SCREAMING_SNAKE_CASE_ ,							)
          UpperCAmelCase          =       PyTorchBenchmark(SCREAMING_SNAKE_CASE_ ,							configs=[config]				)
          UpperCAmelCase          =       benchmark.run()
          self.check_results_dict_not_empty(results.time_inference_result				)
          self.check_results_dict_not_empty(results.memory_inference_result				)




      def      a_   (				self				)    ->       List[str]:
          UpperCAmelCase          =       """sshleifer/tiny-gpt2"""
          UpperCAmelCase          =       AutoConfig.from_pretrained(SCREAMING_SNAKE_CASE_				)
          UpperCAmelCase          =       PyTorchBenchmarkArguments(
              models=[MODEL_ID] ,							training=SCREAMING_SNAKE_CASE_ ,							inference=SCREAMING_SNAKE_CASE_ ,							sequence_lengths=[8] ,							batch_sizes=[1] ,							multi_process=SCREAMING_SNAKE_CASE_ ,							)
          UpperCAmelCase          =       PyTorchBenchmark(SCREAMING_SNAKE_CASE_ ,							configs=[config]				)
          UpperCAmelCase          =       benchmark.run()
          self.check_results_dict_not_empty(results.time_train_result				)
          self.check_results_dict_not_empty(results.memory_train_result				)




      def      a_   (				self				)    ->       List[Any]:
          UpperCAmelCase          =       """sshleifer/tinier_bart"""
          UpperCAmelCase          =       AutoConfig.from_pretrained(SCREAMING_SNAKE_CASE_				)
          UpperCAmelCase          =       PyTorchBenchmarkArguments(
              models=[MODEL_ID] ,							training=SCREAMING_SNAKE_CASE_ ,							inference=SCREAMING_SNAKE_CASE_ ,							sequence_lengths=[8] ,							batch_sizes=[1] ,							multi_process=SCREAMING_SNAKE_CASE_ ,							)
          UpperCAmelCase          =       PyTorchBenchmark(SCREAMING_SNAKE_CASE_ ,							configs=[config]				)
          UpperCAmelCase          =       benchmark.run()
          self.check_results_dict_not_empty(results.time_train_result				)
          self.check_results_dict_not_empty(results.memory_train_result				)




      def      a_   (				self				)    ->       Optional[int]:
          UpperCAmelCase          =       """sshleifer/tiny-gpt2"""
          with tempfile.TemporaryDirectory() as tmp_dir:
              UpperCAmelCase          =       PyTorchBenchmarkArguments(
                  models=[MODEL_ID] ,							training=SCREAMING_SNAKE_CASE_ ,							inference=SCREAMING_SNAKE_CASE_ ,							save_to_csv=SCREAMING_SNAKE_CASE_ ,							sequence_lengths=[8] ,							batch_sizes=[1] ,							inference_time_csv_file=os.path.join(SCREAMING_SNAKE_CASE_ ,							'inf_time.csv'				) ,							train_memory_csv_file=os.path.join(SCREAMING_SNAKE_CASE_ ,							'train_mem.csv'				) ,							inference_memory_csv_file=os.path.join(SCREAMING_SNAKE_CASE_ ,							'inf_mem.csv'				) ,							train_time_csv_file=os.path.join(SCREAMING_SNAKE_CASE_ ,							'train_time.csv'				) ,							env_info_csv_file=os.path.join(SCREAMING_SNAKE_CASE_ ,							'env.csv'				) ,							multi_process=SCREAMING_SNAKE_CASE_ ,							)
              UpperCAmelCase          =       PyTorchBenchmark(SCREAMING_SNAKE_CASE_				)
              benchmark.run()
              self.assertTrue(Path(os.path.join(SCREAMING_SNAKE_CASE_ ,							'inf_time.csv'				)				).exists()				)
              self.assertTrue(Path(os.path.join(SCREAMING_SNAKE_CASE_ ,							'train_time.csv'				)				).exists()				)
              self.assertTrue(Path(os.path.join(SCREAMING_SNAKE_CASE_ ,							'inf_mem.csv'				)				).exists()				)
              self.assertTrue(Path(os.path.join(SCREAMING_SNAKE_CASE_ ,							'train_mem.csv'				)				).exists()				)
              self.assertTrue(Path(os.path.join(SCREAMING_SNAKE_CASE_ ,							'env.csv'				)				).exists()				)




      def      a_   (				self				)    ->       Any:
          UpperCAmelCase          =       """sshleifer/tiny-gpt2"""

          def _check_summary_is_not_empty(lowercase_				):
              self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_ ,							'sequential'				)				)
              self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_ ,							'cumulative'				)				)
              self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_ ,							'current'				)				)
              self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_ ,							'total'				)				)

          with tempfile.TemporaryDirectory() as tmp_dir:
              UpperCAmelCase          =       PyTorchBenchmarkArguments(
                  models=[MODEL_ID] ,							training=SCREAMING_SNAKE_CASE_ ,							inference=SCREAMING_SNAKE_CASE_ ,							sequence_lengths=[8] ,							batch_sizes=[1] ,							log_filename=os.path.join(SCREAMING_SNAKE_CASE_ ,							'log.txt'				) ,							log_print=SCREAMING_SNAKE_CASE_ ,							trace_memory_line_by_line=SCREAMING_SNAKE_CASE_ ,							multi_process=SCREAMING_SNAKE_CASE_ ,							)
              UpperCAmelCase          =       PyTorchBenchmark(SCREAMING_SNAKE_CASE_				)
              UpperCAmelCase          =       benchmark.run()
              _check_summary_is_not_empty(result.inference_summary				)
              _check_summary_is_not_empty(result.train_summary				)
              self.assertTrue(Path(os.path.join(SCREAMING_SNAKE_CASE_ ,							'log.txt'				)				).exists()				)








def  lowerCAmelCase_	(lowerCAmelCase__:		int  ):





			"""simple docstring"""


			if num <= 0:
						raise ValueError("""Input must be a positive integer"""  )

			UpperCAmelCase_:		Tuple							      =     [True] * (num + 1)

			UpperCAmelCase_:		Tuple							      =     2
			while p * p <= num:
						if primes[p]:
									for i in range(p * p		,						num + 1		,						lowerCAmelCase__  ):
												UpperCAmelCase_:		Optional[Any]							      =     False
						p += 1

			return [prime for prime in range(2		,						num + 1  ) if primes[prime]]


if __name__ == "__main__":
	import doctest

	doctest.testmod()

	a      :  Optional[Any]			     =			int(input('Enter a positive integer: ').strip())
	print(prime_sieve_eratosthenes(user_num))






